Overview

Dataset statistics

Number of variables13
Number of observations254312
Missing cells25310
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.2 MiB
Average record size in memory112.0 B

Variable types

Numeric11
DateTime1
Categorical1

Alerts

MP1 FLOW1 (l/s) is highly correlated with MP1 PDEPTH_1 (mm) and 3 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 WATERTEMP_1 (°C) is highly correlated with monthHigh correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
month is highly correlated with MP1 WATERTEMP_1 (°C)High correlation
MP1 FLOW1 (l/s) is highly correlated with MP1 PDEPTH_1 (mm) and 3 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
MP1 WATERTEMP_1 (°C) is highly correlated with monthHigh correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s)High correlation
month is highly correlated with MP1 WATERTEMP_1 (°C)High correlation
MP1 FLOW1 (l/s) is highly correlated with MP1 PDEPTH_1 (mm) and 3 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with MP1 FLOW1 (l/s) and 2 other fieldsHigh correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s)High correlation
Daily Cumulative Rainfall (mm) is highly correlated with MP1 FLOW1 (l/s) and 3 other fieldsHigh correlation
MP1 FLOW1 (l/s) is highly correlated with Daily Cumulative Rainfall (mm) and 4 other fieldsHigh correlation
MP1 PDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UNIDEPTH (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 UpDEPTH_1 (mm) is highly correlated with Daily Cumulative Rainfall (mm) and 3 other fieldsHigh correlation
MP1 WATERTEMP_1 (°C) is highly correlated with monthHigh correlation
Raw Average Velocity (m/s) is highly correlated with MP1 FLOW1 (l/s) and 1 other fieldsHigh correlation
month is highly correlated with MP1 WATERTEMP_1 (°C)High correlation
targets is highly correlated with Raw Average Velocity (m/s)High correlation
MP1 FLOW1 (l/s) has 4213 (1.7%) missing values Missing
MP1 PDEPTH_1 (mm) has 4213 (1.7%) missing values Missing
MP1 UNIDEPTH (mm) has 4213 (1.7%) missing values Missing
MP1 UpDEPTH_1 (mm) has 4213 (1.7%) missing values Missing
MP1 WATERTEMP_1 (°C) has 4213 (1.7%) missing values Missing
Raw Average Velocity (m/s) has 4213 (1.7%) missing values Missing
Final Rainfall (mm) is highly skewed (γ1 = 42.53877637) Skewed
MP1 PDEPTH_1 (mm) is highly skewed (γ1 = 38.566953) Skewed
MP1 UNIDEPTH (mm) is highly skewed (γ1 = 39.01797907) Skewed
date has unique values Unique
Daily Cumulative Rainfall (mm) has 176634 (69.5%) zeros Zeros
Final Rainfall (mm) has 246640 (97.0%) zeros Zeros
MP1 FLOW1 (l/s) has 4243 (1.7%) zeros Zeros
Raw Average Velocity (m/s) has 4243 (1.7%) zeros Zeros
hour has 10597 (4.2%) zeros Zeros

Reproduction

Analysis started2021-12-13 10:15:18.559260
Analysis finished2021-12-13 10:15:42.133796
Duration23.57 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Daily Cumulative Rainfall (mm)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct447
Distinct (%)0.2%
Missing16
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.064516157
Minimum0
Maximum68.5
Zeros176634
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:42.195311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile6.6
Maximum68.5
Range68.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation3.505419053
Coefficient of variation (CV)3.292969328
Kurtosis65.65429192
Mean1.064516157
Median Absolute Deviation (MAD)0
Skewness6.553403856
Sum270702.2005
Variance12.28796273
MonotonicityNot monotonic
2021-12-13T11:15:42.278055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0176634
69.5%
0.211024
 
4.3%
0.15860
 
2.3%
0.44825
 
1.9%
0.33901
 
1.5%
0.63176
 
1.2%
0.52792
 
1.1%
1.61939
 
0.8%
1.21710
 
0.7%
0.81640
 
0.6%
Other values (437)40795
 
16.0%
ValueCountFrequency (%)
0176634
69.5%
0.15860
 
2.3%
0.211024
 
4.3%
0.33901
 
1.5%
0.44825
 
1.9%
0.52792
 
1.1%
0.63176
 
1.2%
0.7846
 
0.3%
0.81640
 
0.6%
0.9755
 
0.3%
ValueCountFrequency (%)
68.51
< 0.1%
68.31
< 0.1%
68.11
< 0.1%
67.91
< 0.1%
67.81
< 0.1%
67.61
< 0.1%
67.41
< 0.1%
67.31
< 0.1%
67.21
< 0.1%
67.11
< 0.1%

Final Rainfall (mm)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct48
Distinct (%)< 0.1%
Missing16
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.007212461069
Minimum0
Maximum9.4
Zeros246640
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:42.363740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9.4
Range9.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08117148965
Coefficient of variation (CV)11.25434008
Kurtosis2825.097805
Mean0.007212461069
Median Absolute Deviation (MAD)0
Skewness42.53877637
Sum1834.1
Variance0.006588810732
MonotonicityNot monotonic
2021-12-13T11:15:42.445339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0246640
97.0%
0.13560
 
1.4%
0.22684
 
1.1%
0.4588
 
0.2%
0.3318
 
0.1%
0.6166
 
0.1%
0.562
 
< 0.1%
0.861
 
< 0.1%
142
 
< 0.1%
1.224
 
< 0.1%
Other values (38)151
 
0.1%
ValueCountFrequency (%)
0246640
97.0%
0.13560
 
1.4%
0.22684
 
1.1%
0.3318
 
0.1%
0.4588
 
0.2%
0.562
 
< 0.1%
0.6166
 
0.1%
0.718
 
< 0.1%
0.861
 
< 0.1%
0.910
 
< 0.1%
ValueCountFrequency (%)
9.41
 
< 0.1%
7.22
< 0.1%
7.11
 
< 0.1%
6.21
 
< 0.1%
5.81
 
< 0.1%
5.62
< 0.1%
5.41
 
< 0.1%
5.21
 
< 0.1%
52
< 0.1%
4.84
< 0.1%

MP1 FLOW1 (l/s)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct241368
Distinct (%)96.5%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean5.006886341
Minimum0
Maximum66.66148
Zeros4243
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:42.527116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5910277
Q13.30221
median4.912778
Q36.24276
95-th percentile8.8093148
Maximum66.66148
Range66.66148
Interquartile range (IQR)2.94055

Descriptive statistics

Standard deviation2.776194198
Coefficient of variation (CV)0.5544751786
Kurtosis64.78056525
Mean5.006886341
Median Absolute Deviation (MAD)1.456098
Skewness4.536605925
Sum1252217.267
Variance7.707254225
MonotonicityNot monotonic
2021-12-13T11:15:42.608966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04243
 
1.7%
5.2857264
 
< 0.1%
4.2718763
 
< 0.1%
5.0135783
 
< 0.1%
6.6319923
 
< 0.1%
6.0650013
 
< 0.1%
3.2389153
 
< 0.1%
3.4841283
 
< 0.1%
4.6787533
 
< 0.1%
5.6885323
 
< 0.1%
Other values (241358)245828
96.7%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
04243
1.7%
0.1226451
 
< 0.1%
0.1433241
 
< 0.1%
0.2088791
 
< 0.1%
0.2464961
 
< 0.1%
0.3302841
 
< 0.1%
0.3575591
 
< 0.1%
0.3630161
 
< 0.1%
0.4153211
 
< 0.1%
0.418981
 
< 0.1%
ValueCountFrequency (%)
66.661481
< 0.1%
66.615171
< 0.1%
65.487691
< 0.1%
65.319371
< 0.1%
64.445381
< 0.1%
64.306391
< 0.1%
64.231351
< 0.1%
64.133351
< 0.1%
64.050451
< 0.1%
64.036231
< 0.1%

MP1 PDEPTH_1 (mm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct9727
Distinct (%)3.9%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean60.65428626
Minimum0
Maximum2604.37
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:42.692923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.37
Q152.19
median59.47
Q366.6
95-th percentile80.68
Maximum2604.37
Range2604.37
Interquartile range (IQR)14.41

Descriptive statistics

Standard deviation46.62398603
Coefficient of variation (CV)0.7686841096
Kurtosis1719.003472
Mean60.65428626
Median Absolute Deviation (MAD)7.2
Skewness38.566953
Sum15169576.34
Variance2173.796073
MonotonicityNot monotonic
2021-12-13T11:15:42.780162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.73137
 
0.1%
58.74128
 
0.1%
61.26127
 
< 0.1%
58.56126
 
< 0.1%
58.97122
 
< 0.1%
58.36122
 
< 0.1%
57.94122
 
< 0.1%
57.83121
 
< 0.1%
60.8121
 
< 0.1%
59.84120
 
< 0.1%
Other values (9717)248853
97.9%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
01
< 0.1%
5.711
< 0.1%
5.891
< 0.1%
5.921
< 0.1%
6.151
< 0.1%
6.451
< 0.1%
6.461
< 0.1%
6.532
< 0.1%
6.571
< 0.1%
6.742
< 0.1%
ValueCountFrequency (%)
2604.371
< 0.1%
2597.591
< 0.1%
2584.691
< 0.1%
2570.911
< 0.1%
2568.811
< 0.1%
2552.141
< 0.1%
2551.71
< 0.1%
2535.911
< 0.1%
2500.161
< 0.1%
2481.311
< 0.1%

MP1 UNIDEPTH (mm)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct8083
Distinct (%)3.2%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean61.01146074
Minimum-253.75
Maximum2604.37
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size3.9 MiB
2021-12-13T11:15:42.863071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-253.75
5-th percentile40.44
Q152.06
median60.29
Q366.83
95-th percentile79.66
Maximum2604.37
Range2858.12
Interquartile range (IQR)14.77

Descriptive statistics

Standard deviation46.43689921
Coefficient of variation (CV)0.7611176432
Kurtosis1745.716519
Mean61.01146074
Median Absolute Deviation (MAD)7.32
Skewness39.01797907
Sum15258905.32
Variance2156.385608
MonotonicityNot monotonic
2021-12-13T11:15:42.941184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.36126
 
< 0.1%
62.35126
 
< 0.1%
62.24125
 
< 0.1%
61.74125
 
< 0.1%
61.96124
 
< 0.1%
61.8124
 
< 0.1%
61.98124
 
< 0.1%
63.75124
 
< 0.1%
64.02124
 
< 0.1%
62.27124
 
< 0.1%
Other values (8073)248853
97.9%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
-253.751
< 0.1%
12.161
< 0.1%
12.451
< 0.1%
12.681
< 0.1%
12.861
< 0.1%
12.911
< 0.1%
12.951
< 0.1%
13.021
< 0.1%
13.111
< 0.1%
13.271
< 0.1%
ValueCountFrequency (%)
2604.371
< 0.1%
2597.591
< 0.1%
2584.691
< 0.1%
2570.911
< 0.1%
2568.811
< 0.1%
2552.141
< 0.1%
2551.71
< 0.1%
2535.911
< 0.1%
2500.161
< 0.1%
2481.311
< 0.1%

MP1 UpDEPTH_1 (mm)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8004
Distinct (%)3.2%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean60.15066026
Minimum-253.75
Maximum435.06
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size3.9 MiB
2021-12-13T11:15:43.126253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-253.75
5-th percentile40.45
Q152.06
median60.29
Q366.83
95-th percentile79.66
Maximum435.06
Range688.81
Interquartile range (IQR)14.77

Descriptive statistics

Standard deviation15.97931283
Coefficient of variation (CV)0.2656548201
Kurtosis228.5955856
Mean60.15066026
Median Absolute Deviation (MAD)7.32
Skewness9.985162944
Sum15043619.98
Variance255.3384385
MonotonicityNot monotonic
2021-12-13T11:15:43.206582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.36126
 
< 0.1%
62.35126
 
< 0.1%
61.74125
 
< 0.1%
62.24125
 
< 0.1%
61.96124
 
< 0.1%
61.8124
 
< 0.1%
62.27124
 
< 0.1%
64.02124
 
< 0.1%
63.75124
 
< 0.1%
61.98124
 
< 0.1%
Other values (7994)248853
97.9%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
-253.751
< 0.1%
25.811
< 0.1%
25.881
< 0.1%
25.941
< 0.1%
261
< 0.1%
26.061
< 0.1%
26.091
< 0.1%
26.161
< 0.1%
26.641
< 0.1%
26.741
< 0.1%
ValueCountFrequency (%)
435.061
< 0.1%
434.951
< 0.1%
434.771
< 0.1%
434.371
< 0.1%
434.271
< 0.1%
434.21
< 0.1%
434.191
< 0.1%
434.142
< 0.1%
434.131
< 0.1%
434.112
< 0.1%

MP1 WATERTEMP_1 (°C)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct142
Distinct (%)0.1%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean16.29478966
Minimum-17.8
Maximum23.3
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size3.9 MiB
2021-12-13T11:15:43.291342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-17.8
5-th percentile12.1
Q114.1
median15.9
Q318.7
95-th percentile20.9
Maximum23.3
Range41.1
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation2.762145857
Coefficient of variation (CV)0.1695109857
Kurtosis-0.9221257188
Mean16.29478966
Median Absolute Deviation (MAD)2.2
Skewness0.1881975
Sum4075310.6
Variance7.629449737
MonotonicityNot monotonic
2021-12-13T11:15:43.369161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.84655
 
1.8%
14.34581
 
1.8%
14.64553
 
1.8%
15.34370
 
1.7%
15.14194
 
1.6%
13.84183
 
1.6%
14.14157
 
1.6%
14.53850
 
1.5%
153797
 
1.5%
15.63650
 
1.4%
Other values (132)208109
81.8%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
-17.81
 
< 0.1%
8.93
 
< 0.1%
91
 
< 0.1%
9.13
 
< 0.1%
9.56
 
< 0.1%
9.64
 
< 0.1%
9.73
 
< 0.1%
9.84
 
< 0.1%
108
< 0.1%
10.115
< 0.1%
ValueCountFrequency (%)
23.33
 
< 0.1%
23.23
 
< 0.1%
23.18
 
< 0.1%
238
 
< 0.1%
22.924
 
< 0.1%
22.820
 
< 0.1%
22.739
< 0.1%
22.637
< 0.1%
22.546
< 0.1%
22.469
< 0.1%

Raw Average Velocity (m/s)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct895
Distinct (%)0.4%
Missing4213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.3843306742
Minimum0
Maximum0.9531
Zeros4243
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:43.452805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.189
Q10.3249
median0.4041
Q30.4563
95-th percentile0.522
Maximum0.9531
Range0.9531
Interquartile range (IQR)0.1314

Descriptive statistics

Standard deviation0.1091561765
Coefficient of variation (CV)0.284016301
Kurtosis1.736174111
Mean0.3843306742
Median Absolute Deviation (MAD)0.063
Skewness-0.8131069782
Sum96120.7173
Variance0.01191507086
MonotonicityNot monotonic
2021-12-13T11:15:43.539221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04243
 
1.7%
0.43831261
 
0.5%
0.4321246
 
0.5%
0.43741228
 
0.5%
0.43921213
 
0.5%
0.43651206
 
0.5%
0.43471195
 
0.5%
0.43561194
 
0.5%
0.43021185
 
0.5%
0.43381184
 
0.5%
Other values (885)234944
92.4%
(Missing)4213
 
1.7%
ValueCountFrequency (%)
04243
1.7%
0.01441
 
< 0.1%
0.0181
 
< 0.1%
0.02611
 
< 0.1%
0.0271
 
< 0.1%
0.02881
 
< 0.1%
0.03062
 
< 0.1%
0.03421
 
< 0.1%
0.04321
 
< 0.1%
0.04411
 
< 0.1%
ValueCountFrequency (%)
0.95311
< 0.1%
0.89461
< 0.1%
0.89011
< 0.1%
0.88291
< 0.1%
0.8821
< 0.1%
0.87751
< 0.1%
0.87571
< 0.1%
0.87481
< 0.1%
0.87391
< 0.1%
0.87122
< 0.1%

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.49995281
Minimum0
Maximum23
Zeros10597
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:43.612352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation6.922248068
Coefficient of variation (CV)0.6019370844
Kurtosis-1.204177677
Mean11.49995281
Median Absolute Deviation (MAD)6
Skewness2.599801282 × 10-5
Sum2924576
Variance47.91751831
MonotonicityNot monotonic
2021-12-13T11:15:43.678176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2310598
 
4.2%
410598
 
4.2%
810597
 
4.2%
610597
 
4.2%
010597
 
4.2%
1210597
 
4.2%
1510596
 
4.2%
2210596
 
4.2%
2110596
 
4.2%
2010596
 
4.2%
Other values (14)148344
58.3%
ValueCountFrequency (%)
010597
4.2%
110596
4.2%
210596
4.2%
310596
4.2%
410598
4.2%
510596
4.2%
610597
4.2%
710596
4.2%
810597
4.2%
910596
4.2%
ValueCountFrequency (%)
2310598
4.2%
2210596
4.2%
2110596
4.2%
2010596
4.2%
1910596
4.2%
1810596
4.2%
1710596
4.2%
1610596
4.2%
1510596
4.2%
1410596
4.2%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73617446
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:43.746438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.805063711
Coefficient of variation (CV)0.5595428375
Kurtosis-1.193283463
Mean15.73617446
Median Absolute Deviation (MAD)8
Skewness0.007382156183
Sum4001898
Variance77.52914695
MonotonicityNot monotonic
2021-12-13T11:15:43.819521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
198354
 
3.3%
148353
 
3.3%
58353
 
3.3%
228353
 
3.3%
18353
 
3.3%
288353
 
3.3%
68352
 
3.3%
188352
 
3.3%
278352
 
3.3%
268352
 
3.3%
Other values (21)170785
67.2%
ValueCountFrequency (%)
18353
3.3%
28352
3.3%
38352
3.3%
48352
3.3%
58353
3.3%
68352
3.3%
78352
3.3%
88352
3.3%
98352
3.3%
108352
3.3%
ValueCountFrequency (%)
315184
2.0%
307489
2.9%
297776
3.1%
288353
3.3%
278352
3.3%
268352
3.3%
258352
3.3%
248352
3.3%
238352
3.3%
228353
3.3%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.926397496
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2021-12-13T11:15:43.890097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.461526366
Coefficient of variation (CV)0.5840860942
Kurtosis-1.167999473
Mean5.926397496
Median Absolute Deviation (MAD)3
Skewness0.2512528015
Sum1507154
Variance11.98216478
MonotonicityNot monotonic
2021-12-13T11:15:43.948270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
526785
10.5%
126784
10.5%
326784
10.5%
425921
10.2%
224482
9.6%
1218144
7.1%
1017858
7.0%
717856
7.0%
817856
7.0%
617281
6.8%
Other values (2)34561
13.6%
ValueCountFrequency (%)
126784
10.5%
224482
9.6%
326784
10.5%
425921
10.2%
526785
10.5%
617281
6.8%
717856
7.0%
817856
7.0%
917280
6.8%
1017858
7.0%
ValueCountFrequency (%)
1218144
7.1%
1117281
6.8%
1017858
7.0%
917280
6.8%
817856
7.0%
717856
7.0%
617281
6.8%
526785
10.5%
425921
10.2%
326784
10.5%

date
Date

UNIQUE

Distinct254312
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
Minimum2018-12-31 00:00:00
Maximum2021-06-01 00:00:00
2021-12-13T11:15:44.024101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:44.110018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

targets
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
0
230080 
1
24232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0230080
90.5%
124232
 
9.5%

Length

2021-12-13T11:15:44.192451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-13T11:15:44.233261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0230080
90.5%
124232
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-13T11:15:39.672397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:27.469463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.693313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.979770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.210293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.383921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.682928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.933202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.065212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.361136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.500615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.783510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:27.591890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.802927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.092571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.319139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.494316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.822177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.041134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.177809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.471246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.614246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.885886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:27.695602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.903111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.195844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.422390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.596296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.929931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.139388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.283652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.573228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.718554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.992677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:27.805130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.008066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.319064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.530423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.703461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.054379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.243670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.393845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.678136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.826687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.191324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:27.913718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.110848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.437446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.637415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.913206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.164161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.345227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.604662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.781511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.932958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.296027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.029693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.356182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.544206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.747732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.016910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.270200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.449256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.712671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.885458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.039543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.403782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.140322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.462740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.655039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.856400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.135387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.381035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.553096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.822758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.991758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.148485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.504933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.246638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.560262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.760361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.958502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.253657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.498067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.649529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:36.926718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.091790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.251189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.609180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.363554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.666452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.873651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.068495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.366247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.610856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.755485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.037895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.196304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.359699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.707727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.471188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.767909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:30.995502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.171504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.471334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.716167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.856614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.143669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.294797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.462078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:40.812857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:28.585509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:29.875099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:31.106242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:32.279339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:33.578438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:34.826253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:35.961990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:37.254076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:38.397435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-13T11:15:39.567062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-13T11:15:44.282041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-13T11:15:44.409047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-13T11:15:44.535779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-13T11:15:44.661906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-13T11:15:40.951359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-13T11:15:41.254562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-13T11:15:41.861716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-13T11:15:42.011576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Daily Cumulative Rainfall (mm)Final Rainfall (mm)MP1 FLOW1 (l/s)MP1 PDEPTH_1 (mm)MP1 UNIDEPTH (mm)MP1 UpDEPTH_1 (mm)MP1 WATERTEMP_1 (°C)Raw Average Velocity (m/s)hourdaymonthdatetargets
00.00.02.74401550.9664.1064.1015.20.1962031122018-12-31 00:00:000
10.00.02.70966563.4164.8564.8515.10.1908031122018-12-31 00:05:000
20.00.02.72057864.2963.5863.5815.10.1971031122018-12-31 00:10:000
30.00.02.39462364.1058.5258.5215.10.1962031122018-12-31 00:15:000
40.00.02.31632963.8957.0257.0215.00.1971031122018-12-31 00:20:000
50.00.01.70139763.7345.5145.5114.90.1998031122018-12-31 00:25:000
60.00.02.26824563.6856.3456.3415.00.1962031122018-12-31 00:30:000
70.00.02.57240263.8459.6559.6515.00.2043031122018-12-31 00:35:000
80.00.02.52845663.7258.9858.9814.80.2043031122018-12-31 00:40:000
90.00.02.21048062.9057.5057.5014.80.1854031122018-12-31 00:45:000

Last rows

Daily Cumulative Rainfall (mm)Final Rainfall (mm)MP1 FLOW1 (l/s)MP1 PDEPTH_1 (mm)MP1 UNIDEPTH (mm)MP1 UpDEPTH_1 (mm)MP1 WATERTEMP_1 (°C)Raw Average Velocity (m/s)hourdaymonthdatetargets
2543020.00.00.0000083.8379.5079.5015.40.0000233152021-05-31 23:15:001
2543030.00.04.3106985.1379.4379.4315.30.2430233152021-05-31 23:20:001
2543040.00.00.0000085.3679.3579.3515.40.0000233152021-05-31 23:25:001
2543050.00.00.0000084.3079.1979.1915.40.0000233152021-05-31 23:30:001
2543060.00.00.0000083.8279.0779.0715.30.0000233152021-05-31 23:35:001
2543070.00.00.0000083.9278.8878.8815.30.0000233152021-05-31 23:40:001
2543080.00.00.0000083.6678.7078.7015.30.0000233152021-05-31 23:45:001
2543090.00.03.9666983.7578.6578.6515.30.2268233152021-05-31 23:50:001
2543100.00.00.0000083.9078.5478.5415.30.0000233152021-05-31 23:55:001
2543110.00.00.0000082.9678.4478.4415.20.00000162021-06-01 00:00:001